A machine learning based methodology for broken rail prediction on freight railroads: A case study in the United States

Abstract

Predicting the occurrence of broken rails has safety and economic benefits, and reduces accidents and service disruptions. This paper aims to build a data-driven model for broken rail prediction using data related to infrastructure, operations, inspections, and weather conditions from 2013 to 2019. The railroad data was provided by one major Class I U.S. railroad. The weather condition data was collected from the National Oceanic and Atmospheric Administration (NOAA). Based on time-series data partitioning, three different machine learning models are developed for predicting broken rail occurrence one month in advance. The selected models, including logistic regression, random forests, and gradient boosting, are trained using data from 2013 to 2018. The performance of three trained models are evaluated using the data from 2019. The relationship between the percentage of the network scanned and the percentage of broken rails found is used to identify locations that are more prone to broken rails. The findings of this study show that the gradient boosting model performs better than the other two methods for our datasets. The model also identifies that the number of detected rail defects within last 365 days, minimum ambient temperature of the last 30 days, days from the last broken rail, segment length, traffic density and other factors have significant influences on prediction results. Using this model, 40.4% of broken rails can be successfully predicted one month in advance when focusing on 10% of the railroad network scanned. The model can potentially be used to prioritize inspection and maintenance activities on broken-rail-prone locations, and thus to further improve infrastructure safety given budgetary constraints.